Artificial intelligence is revolutionizing the IT sector, and Large Language Models (LLMs) have proven to be powerful tools to increase automation, security and efficiency in technology infrastructure management. Traditionally, LLMs have been used in natural language processing (NLP). However, their ability to analyze large volumes of data and detect patterns also makes them valuable in IT.
In this article, we will explore how LLMs can improve IT network management and security, focusing on two key applications: the analysis and classification of network logs and the detection of anomalous patterns in HTTP/S and DNS traffic.
The Role of LLMs in IT Network Management and Security
The IT sector is constantly focusing on managing and securing complex networks. The amount of data generated by network devices, servers, applications and users is overwhelming, and IT security and administration teams need tools to help them identify problems before they become a serious issue. One of the biggest challenges is log analysis and network traffic monitoring. In large environments, IT teams receive millions of events daily (a figure that is imposible to effectively review manually). This is where LLMs can make a difference, providing advanced capabilities to automatically and accurately classify, prioritize and analyze data.
Key LLM Applications in IT
Analysis and Classification of Network Logs
IT systems generate an enormous amount of logs, from firewall and server events to access and user activity logs. LLMs can analyze these logs automatically and classify them into different categories:
– Normal events: Recurrent and non-risky activities.
– Suspicious events: Unusual patterns that could indicate a threat.
– Critical events: Activities confirmed as malicious or problematic.
Language models can be trained with historical logs and learn to distinguish between normal events and potential security incidents. For example, if a firewall detects recurring connections from an unknown IP with failed authentication attempts, the LLM could classify it as an attempt at a brute force attack and generate an automated alert.
Another benefit is the generation of intelligible summaries for security analysts. Instead of presenting an endless list of logs, an LLM can structure a report as:
“An increase in failed login attempts from IP 192.168.1.20 to the database server was detected. This behavior is similar to previous brute force attacks detected on the network.”
This allows security teams to focus on the most critical events and reduce noise in warning systems.
Detection of Anomalous Patterns in HTTP/S and DNS Traffic
Network security heavily relies on the detection of anomalous traffic. Many advanced threats disguise themselves within web and DNS traffic to evade traditional detection systems. LLMs can analyze HTTP/S requests and DNS queries to identify suspicious patterns and act before malware causes damage.
Some applications include:
– Identifying malicious domains: Attacks based on Domain Generation Algorithms (DGA) create thousands of random domains to communicate with Command & Control (C2) servers. An LLM can detect suspicious domain names and block them before the malware can establish a connection.
Example: If the model detects that an endpoint queries domains like `xkgf23abq.com` and `sdjkwe4md.net`, it can match them with known malware patterns and generate an alert.
– Analysis of suspicious HTTP headers: Many malicious connections use User-Agents or atypical HTTP headers to avoid detection. An LLM can learn which combinations of headers are often associated with attacks.
Example: If an incoming connection uses an unusual User-Agent and a suspicious HTTP request structure, the model can flag it as a possible threat.
– Unusual traffic monitoring: An LLM can learn what type of traffic is normal for an organization and detect deviations.
Example: If a user who normally accesses internal resources starts sending large amounts of data to servers in a foreign country, data exfiltration could be involved.
These capabilities allow security teams to act before an attack has serious consequences, improving network protection against advanced threats.
Conclusion on LLMS in IT
LLMs in the IT sector are transforming the way networks are managed and secured. From intelligent log analysis to early detection of suspicious traffic, these models can help IT teams improve the efficiency and security of their infrastructures. Teldat includes these innovative technologies in its be.Safe XDR solution, which helps detect anomalies long before they occur.
